Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Probabilistic Optimization of Top N Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Aggregate computation over data streams
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
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Practical applications often need to rank multi-variate records by assigning various priorities to different attributes. Consider a relation that stores students' grades on two courses: database and algorithm. Student performance is evaluated by an “overall score” calculated as w1 · gdb + w2 · galg, where w1, w2 are two input “weights”, and gdb (galg) is the student grade on database (algorithm). A “top-k ranked query” retrieves the k students with the best scores according to specific w1 and w2. We focus on top-k queries whose k is bounded by a constant c, and present solutions that guarantee low worst-case query cost by using provably the minimum space. The core of our methods is a novel concept, “minimum covering subset”, which contains only the necessary data for ensuring correct answers for all queries. Any 2D ranked search, for example, can be processed in O(logB (m/B) + c/B) I/Os using O(m/B) space, where m is the size of the minimum covering subset, and B the disk page capacity. Similar results are also derived for higher dimensionalities and approximate ranked retrieval.